A framework for considering prior information in network-based approaches to omics data analysis.

For decades, molecular biologists have been uncovering the mechanics of biological systems. Efforts to bring their findings together have led to the development of multiple databases and information systems that capture and present pathway information in a computable network format. Concurrently, the advent of modern omics technologies has empowered researchers to systematically profile cellular processes across different modalities. Numerous algorithms, methodologies, and tools have been developed to use prior knowledge networks (PKNs) in the analysis of omics datasets. Interestingly, it has been repeatedly demonstrated that the source of prior knowledge can greatly impact the results of a given analysis. For these methods to be successful it is paramount that their selection of PKNs is amenable to the data type and the computational task they aim to accomplish. Here we present a five-level framework that broadly describes network models in terms of their scope, level of detail, and ability to inform causal predictions. To contextualize this framework, we review a handful of network-based omics analysis methods at each level, while also describing the computational tasks they aim to accomplish.

A prognostic matrix gene expression signature defines functional glioblastoma phenotypes and niches.

Background: Interactions among tumor, immune, and vascular niches play major roles in driving glioblastoma (GBM) malignancy and treatment responses. The composition, heterogeneity, and localization of extracellular core matrix proteins (CMPs) that mediate such interactions, however, are not well understood. Methods: Here, through computational genomics and proteomics approaches, we analyzed the functional and clinical relevance of CMP expression in GBM at bulk, single cell, and spatial anatomical resolution. Results: We identified genes encoding CMPs whose expression levels categorize GBM tumors into CMP expression-high (M-H) and CMP expression-low (M-L) groups. CMP enrichment is associated with worse patient survival, specific driver oncogenic alterations, mesenchymal state, infiltration of pro-tumor immune cells, and immune checkpoint gene expression. Anatomical and single-cell transcriptome analyses indicate that matrisome gene expression is enriched in vascular and leading edge/infiltrative niches that are known to harbor glioma stem cells driving GBM progression. Finally, we identified a 17-gene CMP expression signature, termed Matrisome 17 (M17) signature that further refines the prognostic value of CMP genes. The M17 signature is a significantly stronger prognostic factor compared to MGMT promoter methylation status as well as canonical subtypes, and importantly, potentially predicts responses to PD1 blockade. Conclusion: The matrisome gene expression signature provides a robust stratification of GBM patients by survival and potential biomarkers of functionally relevant GBM niches that can mediate mesenchymal-immune cross talk. Patient stratification based on matrisome profiles can contribute to selection and optimization of treatment strategies.

Adenomyosis: single-cell transcriptomic analysis reveals a paracrine mesenchymal-epithelial interaction involving the WNT/SFRP pathway.

OBJECTIVE: To assess the cellular and molecular landscape of adenomyosis. DESIGN: Single-cell analysis of genome-wide messenger RNA (mRNA) expression (single-cell RNA sequencing) of matched tissues of endometrium, adenomyosis, and myometrium using relatively large numbers of viable cells. SETTING: Not applicable. PATIENT(S): Patients (n = 3, age range 40-44 years) undergoing hysterectomy for diffuse adenomyosis. MAIN OUTCOME MEASURE(S): Definition of the molecular landscape of matched adenomyotic, endometrial and myometrial tissues from the same uterus using single-cell RNA sequencing and comparison of distinct cell types in these tissues to identify disease-specific cell populations, abnormal gene expression and pathway activation, and mesenchymal-epithelial interactions. RESULT(S): The largest cell population in the endometrium was composed of closely clustered fibroblast groups, which comprise 36% of all cells and seem to originate from pericyte progenitors differentiating to estrogen/progesterone receptor-expressing endometrial stromal- cells. In contrast, the entire fibroblast population in adenomyosis comprised a larger (50%) portion of all cells and was not linked to any pericyte progenitors. Adenomyotic fibroblasts eventually differentiate into extracellular matrix protein-expressing fibroblasts and smooth muscle cells. Hierarchical clustering of mRNA expression revealed a unique adenomyotic fibroblast population that clustered transcriptomically with endometrial fibroblasts, suggestive of an endometrial stromal cell population serving as progenitors of adenomyosis. Four other adenomyotic fibroblast clusters with disease-specific transcriptomes were distinct from those of endometrial or myometrial fibroblasts. The mRNA levels of the natural WNT inhibitors, named, secreted frizzled-related proteins 1, 2, and 4, were higher in these 4 adenomyotic fibroblast clusters than in endometrial fibroblast clusters. Moreover, we found that multiple WNTs, which originate from fibroblasts and target ciliated and unciliated epithelial cells and endothelial cells, constitute a critical paracrine signaling network in adenomyotic tissue. Compared with endometrial tissue, unciliated and ciliated epithelial cells in adenomyosis comprised a significantly smaller portion of this tissue and exhibited molecular evidence of progesterone resistance and diminished regulation of estrogen signaling. CONCLUSION(S): We found a high degree of heterogeneity in fibroblast-like cells in the adenomyotic uterus. The WNT signaling involving differential expression of secreted frizzled-related proteins, which act as decoy receptors for WNTs, in adenomyotic fibroblasts may have a key role in the pathophysiology of this disease.

A prognostic matrix code defines functional glioblastoma phenotypes and niches.

Interactions among tumor, immune and vascular niches play major roles in driving glioblastoma (GBM) malignancy and treatment responses. The composition, heterogeneity, and localization of extracellular core matrix proteins (CMPs) that mediate such interactions, however, are not well understood. Here, we characterize functional and clinical relevance of genes encoding CMPs in GBM at bulk, single cell, and spatial anatomical resolution. We identify a "matrix code" for genes encoding CMPs whose expression levels categorize GBM tumors into matrisome-high and matrisome-low groups that correlate with worse and better patient survival, respectively. The matrisome enrichment is associated with specific driver oncogenic alterations, mesenchymal state, infiltration of pro-tumor immune cells and immune checkpoint gene expression. Anatomical and single cell transcriptome analyses indicate that matrisome gene expression is enriched in vascular and leading edge/infiltrative anatomic structures that are known to harbor glioma stem cells driving GBM progression. Finally, we identified a 17-gene matrisome signature that retains and further refines the prognostic value of genes encoding CMPs and, importantly, potentially predicts responses to PD1 blockade in clinical trials for GBM. The matrisome gene expression profiles provide potential biomarkers of functionally relevant GBM niches that contribute to mesenchymal-immune cross talk and patient stratification which could be applied to optimize treatment responses.

A prognostic matrix code defines functional glioblastoma phenotypes and niches.

Interactions among tumor, immune and vascular niches play major roles in driving glioblastoma (GBM) malignancy and treatment responses. The composition, heterogeneity, and localization of extracellular core matrix proteins (CMPs) that mediate such interactions, however, are not well understood. Here, we characterize functional and clinical relevance of genes encoding CMPs in GBM at bulk, single cell, and spatial anatomical resolution. We identify a "matrix code" for genes encoding CMPs whose expression levels categorize GBM tumors into matrisome-high and matrisome-low groups that correlate with worse and better survival, respectively, of patients. The matrisome enrichment is associated with specific driver oncogenic alterations, mesenchymal state, infiltration of pro-tumor immune cells and immune checkpoint gene expression. Anatomical and single cell transcriptome analyses indicate that matrisome gene expression is enriched in vascular and leading edge/infiltrative anatomic structures that are known to harbor glioma stem cells driving GBM progression. Finally, we identified a 17-gene matrisome signature that retains and further refines the prognostic value of genes encoding CMPs and, importantly, potentially predicts responses to PD1 blockade in clinical trials for GBM. The matrisome gene expression profiles may provide biomarkers of functionally relevant GBM niches that contribute to mesenchymal-immune cross talk and patient stratification to optimize treatment responses.

ISL2 is a putative tumor suppressor whose epigenetic silencing reprograms the metabolism of pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDA) cells reprogram their transcriptional and metabolic programs to survive the nutrient-poor tumor microenvironment. Through in vivo CRISPR screening, we discovered islet-2 (ISL2) as a candidate tumor suppressor that modulates aggressive PDA growth. Notably, ISL2, a nuclear and chromatin-associated transcription factor, is epigenetically silenced in PDA tumors and high promoter DNA methylation or its reduced expression correlates with poor patient survival. The exogenous ISL2 expression or CRISPR-mediated upregulation of the endogenous loci reduces cell proliferation. Mechanistically, ISL2 regulates the expression of metabolic genes, and its depletion increases oxidative phosphorylation (OXPHOS). As such, ISL2-depleted human PDA cells are sensitive to the inhibitors of mitochondrial complex I in vitro and in vivo. Spatial transcriptomic analysis shows heterogeneous intratumoral ISL2 expression, which correlates with the expression of critical metabolic genes. These findings nominate ISL2 as a putative tumor suppressor whose inactivation leads to increased mitochondrial metabolism that may be exploitable therapeutically.

Expression Levels of SMAD Specific E3 Ubiquitin Protein Ligase 2 (Smurf2) and its Interacting Partners Show Region-specific Alterations During Brain Aging.

Aging occurs due to a combination of several factors, such as telomere attrition, cellular senescence, and stem cell exhaustion. The telomere attrition-dependent cellular senescence is regulated by increased levels of SMAD specific E3 ubiquitin protein ligase 2 (smurf2). With age smurf2 expression increases and Smurf2 protein interacts with several regulatory proteins including, Smad7, Ep300, Yy1, Sirt1, Mdm2, and Tp53, likely affecting its function related to cellular aging. The current study aimed at analyzing smurf2 expression in the aged brain because of its potential regulatory roles in the cellular aging process. Zebrafish were used because like humans they age gradually and their genome has 70% similarity. In the current study, we demonstrated that smurf2 gene and protein expression levels altered in a region-specific manner during the aging process. Also, in both young and old brains, Smurf2 protein was enriched in the cytosol. These results imply that during aging Smurf2 is regulated by several mechanisms including post-translational modifications (PTMs) and complex formation. Also, the expression levels of its interacting partners defined by the STRING database, tp53, mdm2, ep300a, yy1a, smad7, and sirt1, were analyzed. Multivariate analysis indicated that smurf2, ep300a, and sirt1, whose proteins regulate ubiquitination, acetylation, and deacetylation of target proteins including Smad7 and Tp53, showed age- and brain region-dependent patterns. Our data suggest a likely balance between Smurf2- and Mdm2-mediated ubiquitination, and Ep300a-mediated acetylation/Sirt1-mediated deacetylation, which most possibly affects the functionality of other interacting partners in regulating cellular and synaptic aging and ultimately cognitive dysfunction.